Data Analytics Intern

This internship brings together data analytics, business intelligence, engineering data, and modern data platforms in a single R&D environment. Candidates will work with enterprise datasets and reporting solutions while gaining exposure to technologies such as Databricks, Python, Power BI, Power Apps, and advanced analytics.

🔍 What You’ll Work On

The role involves turning large and varied datasets into information that engineering and business teams can use. Data may come from warranty systems, telematics, engineering records, bill of materials, product information, and operational sources.

A major part of the work will involve collecting, integrating, validating, and analyzing data from multiple enterprise systems. The focus is not simply on producing reports, but on making the underlying information reliable enough to support product-quality and operational decisions.

Candidates may also work on trend analysis, Pareto analysis, root-cause investigations, predictive analytics, and performance benchmarking.

Real-world engineering and operational data exposure

📊 Analytics & Reporting Environment

The internship has a strong Business Intelligence and reporting component. Interns will support dashboards, KPI scorecards, and analytics platforms used for leadership and operational visibility.

This includes understanding how business metrics are defined, preparing reliable datasets, and presenting results through visualization tools. Reporting automation is also part of the role, with the position involving technologies such as Power BI, Power Query, Excel, and Qlik Sense.

A useful example is a product-quality dashboard that brings together warranty and telematics information to identify recurring issues and help teams investigate performance trends.

🧠 Technology Exposure

The position combines several areas of the modern data stack:

Python SQL Databricks Power BI Power Query Excel Power Apps Qlik Sense

The job description also mentions Azure DevOps, Microsoft 365, Microsoft Azure Databricks, enterprise reporting ecosystems, Generative AI, and Machine Learning as part of the broader technology environment.

This makes the internship relevant to candidates interested in the intersection of Data Analytics, Data Engineering, Business Intelligence, and AI.

⚙️ Business Problems You May Analyze

The data handled by this team is connected to agricultural products and their operational performance. The work therefore extends beyond standard classroom datasets.

Potential analytical areas described for the role include:

• Warranty analysis to understand product-quality patterns.

• Telematics analysis involving connected-product information.

• Engineering performance analysis to identify trends and improvement areas.

• Bill of material data analysis.

• Operational performance benchmarking.

• Root-cause investigations for quality or productivity issues.

• Predictive analytics for identifying potential patterns.

• KPI monitoring through dashboards and scorecards.

The emphasis is on producing analysis that can support practical product and business decisions rather than creating dashboards without a defined business purpose.

🤝 Cross-Functional Collaboration

Data analytics in this position is closely connected with other business functions. The role specifically involves collaboration with Engineering, Manufacturing, Finance, Product Management, and Quality teams.

That means communication is an important part of the internship. A candidate may need to understand what a stakeholder is trying to measure, determine which data should be used, validate the metric, and then communicate the result through a dashboard or analytical report.

Strong analytics work requires both reliable data and the ability to explain what the data means to the people using it.

The job description also specifically calls for strong written and verbal communication skills and the ability to work with cross-functional stakeholders.

🏗 Digital & Low-Code Development

The internship is not limited to traditional data analysis. CNH Industrial also mentions Power Apps and automated workflows for streamlining business operations and reducing manual effort.

This gives candidates exposure to a low-code development environment alongside traditional programming and analytics.

For example, a repetitive business process could potentially be converted into a Power Apps-based workflow instead of requiring employees to manually maintain spreadsheets and perform the same steps repeatedly.

🎓 Education & Candidate Profile

The stated preferred qualification is a Bachelor’s degree or postgraduate qualification in Engineering, particularly Mechanical, Electrical, Computer Science, or a similar discipline.

The role is particularly relevant for candidates who have academic or project experience in:

Data Analytics • Data Engineering • Business Intelligence • Python • SQL • Data Visualization • Machine Learning

Fully On-Site — Gurgaon

The listing identifies the position as Intern or Co-Op and specifies a fully on-site working mode.

📈 Skills Worth Strengthening

Candidates preparing for this type of internship can focus on practical skills rather than only theoretical knowledge.

SQL: joins, aggregations, CTEs, window functions, data validation, and analytical queries.

Python: Pandas, data cleaning, exploratory analysis, automation, and working with structured datasets.

Power BI: data modeling, Power Query, DAX fundamentals, KPI dashboards, and effective visualization.

Databricks: notebooks, data processing, Spark fundamentals, and working with large datasets.

Analytics: trend analysis, Pareto analysis, root-cause analysis, benchmarking, and KPI design.

Communication: explaining analytical findings clearly to technical and non-technical stakeholders.

🧩 What Recruiters May Evaluate

For an internship combining analytics and engineering data, candidates should be prepared to demonstrate that they can work through a complete analytical problem rather than only write individual queries.

A practical discussion could involve taking raw operational data, identifying data-quality problems, transforming the data, calculating meaningful KPIs, and presenting the findings through a dashboard.

Candidates should also be prepared to explain their academic projects clearly, including the problem, dataset, technologies used, transformations performed, analytical approach, and final outcome.

🌱 R&D Environment

CNH Industrial's India Technology Center in Gurugram is part of its global R&D footprint and focuses on product development and digital solutions. CNH has described the center as supporting areas including software, embedded technologies, data analytics, cloud, automation, simulation, and other technology-driven engineering capabilities.

For a data-focused intern, this places analytics work within a broader engineering and product-development environment rather than an isolated reporting function.

🔑 Keywords for Resume

Python • SQL • Databricks • Power BI • Power Query • Excel • Power Apps • Qlik Sense • Data Analytics • Data Engineering • Business Intelligence • Data Visualization • KPI Management • Dashboard Development • Reporting Automation • Machine Learning • Generative AI • Predictive Analytics • Telematics Data • Warranty Analytics • Root Cause Analysis • Data Validation • Azure DevOps • Microsoft Azure • Data Architecture • Advanced Analytics

💡 Career Takeaway

This CNH Industrial internship combines data analytics, business intelligence, engineering data, and modern data-platform technologies. Candidates interested in Data Analytics, Data Engineering, BI, or analytics-focused R&D can gain exposure to both technical tools and the business context behind enterprise data. The role is particularly centered on Python, SQL, Databricks, Power BI, reporting automation, KPI management, and cross-functional analytics.